报告时间：5月27日，周三，下午4:00-5:00

报告地点：信息工程学院D533学术报告厅

报告题目：基于动力学的因果分析理论与方法

报  告  人：陈洛南研究员

报告摘要：

Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: hase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data.

报告人简介

陈洛南研究员研究方向为生物信息学，计算系统生物学，非线性动力学，机器学习及人工智能等领域。近年，主要开展了数据驱动的复杂系统临界状态预警，基于小样本高维数据的预见性机器学习，基于动力学的因果关系构建，异源数据整合及多层次网络整合、深度学习及人工智能计算的研究等。该研究对系统生物学的核心理论体系构建、脑科学、预防性医疗和个体医疗具有重要意义，在计算系统生物学和复杂疾病研究领域发表了350余篇SCI期刊论文及10余部专著及编著书籍，被引用17000 余次，开发分子生物网络的30余软件。

陈洛南研究员现任中国科学院系统生物学重点实验室执行主任，中国运筹学会《计算系统生物学分会》理事长，IEEE-SMC《系统生物学委员会》主席，中日韩国际系统生物学会组织的轮值主席，中国细胞生物学会《功能基因组学与系统生物学分会》副会长，国家基金委重大研究计划专家组专家，国家自然科学基金“生物信息学”创新群体顾问专家，国家重点研发计划首席科学家。